code: def test_step_method_redirection():
      """Test that the FabricModule redirects the special `LightningModule.*_step` methods through the forward-
      module."""
  
      class DDP(torch.nn.Module):
          def __init__(self, module):
              super().__init__()
              self.module = module
  
          def forward(self, *args, **kwargs):
              return self.module(*args, **kwargs)
  
      class LightningModule(torch.nn.Module):
          def forward(self):
              return "forward_return"
  
          def training_step(self, arg, kwarg=None):
              assert self() == "forward_return"
              assert arg == "train_arg"
              assert kwarg == "train_kwarg"
              return "training_step_return"
  
          def validation_step(self, arg, kwarg=None):
              assert self() == "forward_return"
              assert arg == "val_arg"
              assert kwarg == "val_kwarg"
              return "validation_step_return"
  
          def normal_method(self):
              pass
  
      precision = Mock(wraps=Precision())
      original_module = LightningModule()
      forward_module = DDP(original_module)
      fabric_module = _FabricModule(
          forward_module=forward_module,
          precision=precision,
          original_module=original_module,
      )
  
      # Regular methods on the original_module are visible and identical on the fabric_module ...
      assert (
          fabric_module.normal_method.__wrapped__
          == original_module.normal_method
      )
  
      # ... but special methods like training_step get redirected to the forward_module
      assert (
          fabric_module.training_step.__name__ == "call_forward_module"
      )
      assert (
          fabric_module.validation_step.__name__
          == "call_forward_module"
      )
      assert fabric_module.test_step.__name__ == "call_forward_module"
      assert (
          fabric_module.predict_step.__name__ == "call_forward_module"
      )
  
      with pytest.raises(
          AttributeError, match="has no attribute 'predict_step'"
      ):
          # A special method that does not exist will raise its AttributeError when being called
          fabric_module.predict_step()
  
      # The forward method on the original module remains untouched
      assert original_module.forward.__name__ == "forward"
  
      # The special methods get redirected correctly to produce the expected output
      assert (
          fabric_module.training_step("train_arg", kwarg="train_kwarg")
          == "training_step_return"
      )
      assert (
          fabric_module.training_step("train_arg", kwarg="train_kwarg")
          == "training_step_return"
      )  # call 2nd time
      assert (
          fabric_module.validation_step("val_arg", kwarg="val_kwarg")
          == "validation_step_return"
      )
      precision.forward_context.assert_called()
  
      # The forward method remains untouched/unpatched after the special methods have been called
      assert original_module.forward.__name__ == "forward"
  
      # Special case: forward_module == original_module -> no special treatment applied
      fabric_module = _FabricModule(
          forward_module=original_module,
          precision=Mock(),
          original_module=original_module,
      )
      assert (
          fabric_module.training_step == original_module.training_step
      )
      assert (
          fabric_module.validation_step
          == original_module.validation_step
      )
  
imports: from unittest import mock
  from unittest.mock import Mock, call
  import pytest
  import torch
  from lightning.fabric.fabric import Fabric
  from lightning.fabric.plugins import Precision
  from lightning.fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
  from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer, _unwrap_objects, is_wrapped
  from torch.utils.data import BatchSampler, DistributedSampler
  from torch.utils.data.dataloader import DataLoader
  from tests_fabric.helpers.runif import RunIf
label_desc: Use image node_id 18 for calling a global function with example usage: test_step_method_redirection() and returns: self, str, str, str